Biostat 203B Homework 3

Due Feb 21 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.3.0    fastmap_1.1.1     cli_3.6.3        
 [5] tools_4.3.0       htmltools_0.5.8.1 rstudioapi_0.14   yaml_2.3.8       
 [9] rmarkdown_2.29    knitr_1.45        jsonlite_1.8.8    xfun_0.50        
[13] digest_0.6.34     rlang_1.1.4       evaluate_0.23    

Load necessary libraries (you can add more as needed).

library(arrow)

Warning: package 'arrow' was built under R version 4.3.3

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library(gtsummary)

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library(memuse)

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library(pryr)

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library(R.utils)

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Loading required package: R.methodsS3

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R.oo v1.26.0 (2024-01-24 05:12:50 UTC) successfully loaded. See ?R.oo for help.

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library(tidyverse)

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── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.2     ✔ tidyr     1.3.1
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggplot2)
library(dplyr)
library(lubridate)
library(stringr)
library(duckdb)

Warning: package 'duckdb' was built under R version 4.3.3

Loading required package: DBI

library(tidyr)

Display your machine memory.

memuse::Sys.meminfo()

Totalram:  16.000 GiB 
Freeram:    3.967 GiB 

In this exercise, we use tidyverse (ggplot2, dplyr, etc) to explore the MIMIC-IV data introduced in homework 1 and to build a cohort of ICU stays.

Q1. Visualizing patient trajectory

Visualizing a patient’s encounters in a health care system is a common task in clinical data analysis. In this question, we will visualize a patient’s ADT (admission-discharge-transfer) history and ICU vitals in the MIMIC-IV data.

Q1.1 ADT history

A patient’s ADT history records the time of admission, discharge, and transfer in the hospital. This figure shows the ADT history of the patient with subject_id 10001217 in the MIMIC-IV data. The x-axis is the calendar time, and the y-axis is the type of event (ADT, lab, procedure). The color of the line segment represents the care unit. The size of the line segment represents whether the care unit is an ICU/CCU. The crosses represent lab events, and the shape of the dots represents the type of procedure. The title of the figure shows the patient’s demographic information and the subtitle shows top 3 diagnoses.

Do a similar visualization for the patient with subject_id 10063848 using ggplot.

Hint: We need to pull information from data files patients.csv.gz, admissions.csv.gz, transfers.csv.gz, labevents.csv.gz, procedures_icd.csv.gz, diagnoses_icd.csv.gz, d_icd_procedures.csv.gz, and d_icd_diagnoses.csv.gz. For the big file labevents.csv.gz, use the Parquet format you generated in Homework 2. For reproducibility, make the Parquet folder labevents_pq available at the current working directory hw3, for example, by a symbolic link. Make your code reproducible.

Solution:

patients <- read_csv("~/mimic/hosp/patients.csv.gz")

Rows: 364627 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): gender, anchor_year_group
dbl  (3): subject_id, anchor_age, anchor_year
date (1): dod

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

admissions <- read_csv("~/mimic/hosp/admissions.csv.gz")

Rows: 546028 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl  (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

transfers <- read_csv("~/mimic/hosp/transfers.csv.gz")

Rows: 2413581 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): eventtype, careunit
dbl  (3): subject_id, hadm_id, transfer_id
dttm (2): intime, outtime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

labevents_pq <- read_parquet("labevents_pq/part-0.parquet")
procedures <- read_csv("~/mimic/hosp/procedures_icd.csv.gz")

Rows: 859655 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): icd_code
dbl  (4): subject_id, hadm_id, seq_num, icd_version
date (1): chartdate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

diagnoses <- read_csv("~/mimic/hosp/diagnoses_icd.csv.gz")

Rows: 6364488 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): icd_code
dbl (4): subject_id, hadm_id, seq_num, icd_version

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

d_icd_procedures <- read_csv("~/mimic/hosp/d_icd_procedures.csv.gz")

Rows: 86423 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): icd_code, long_title
dbl (1): icd_version

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

d_icd_diagnoses <- read_csv("~/mimic/hosp/d_icd_diagnoses.csv.gz")

Rows: 112107 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): icd_code, long_title
dbl (1): icd_version

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

icustays <- read_csv("~/mimic/icu/icustays.csv.gz")

Rows: 94458 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): first_careunit, last_careunit
dbl  (4): subject_id, hadm_id, stay_id, los
dttm (2): intime, outtime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

d_items <- read_csv("~/mimic/icu/d_items.csv.gz")

Rows: 4095 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (6): label, abbreviation, linksto, category, unitname, param_type
dbl (3): itemid, lownormalvalue, highnormalvalue

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

chartevents_pq <- read_parquet("chartevents_filtered.parquet")

# Filter data for patient 10063848
subject_id <- 10063848
patient_info <- patients %>% filter(subject_id == !!subject_id)
admissions_info <- admissions %>% filter(subject_id == !!subject_id)
transfers_info <- transfers %>% filter(subject_id == !!subject_id)
labevents_info <- labevents_pq %>% filter(subject_id == !!subject_id)
procedures_info <- procedures %>% filter(subject_id == !!subject_id)
diagnoses_info <- diagnoses %>% filter(subject_id == !!subject_id)

plot

diagnoses_info <- diagnoses_info %>%
  mutate(icd_code = str_pad(icd_code, width = 5, pad = "0"))

diagnoses_info <- diagnoses_info %>% 
  left_join(d_icd_diagnoses, by = c("icd_code", "icd_version"))

long_title_col <- grep("long_title", colnames(diagnoses_info), value = TRUE)

if ("long_title" %in% long_title_col) {
  diagnoses_info <- diagnoses_info %>% rename(diagnosis_name = long_title)
} else if ("long_title.x" %in% long_title_col) {
  diagnoses_info <- diagnoses_info %>% rename(diagnosis_name = long_title.x)
} else if ("long_title.y" %in% long_title_col) {
  diagnoses_info <- diagnoses_info %>% rename(diagnosis_name = long_title.y)
} else {
  stop("long_title column not found in diagnoses_info")
}

top_diagnoses <- diagnoses_info %>% 
  filter(!is.na(diagnosis_name)) %>% 
  count(diagnosis_name, sort = TRUE) %>%  
  head(3) %>% 
  pull(diagnosis_name)

top_diagnoses_text <- ifelse(length(top_diagnoses) > 0,
  paste(top_diagnoses, collapse = "\n"), "")

patient_summary <- paste(
  "Patient", 
  subject_id, 
  ifelse(is.na(patient_info$gender), "", patient_info$gender), 
  ifelse(is.na(patient_info$anchor_age), "",
  paste(patient_info$anchor_age, "years old"))
) %>% str_squish() 

transfers_info <- transfers_info %>%
  mutate(intime = as.POSIXct(intime, format="%Y-%m-%d %H:%M:%S"),
         outtime = as.POSIXct(outtime, format="%Y-%m-%d %H:%M:%S")) %>%
  filter(!is.na(outtime))

labevents_info <- labevents_info %>%
  mutate(chartdate = as.POSIXct(charttime, format="%Y-%m-%d %H:%M:%S"))

procedures_info <- procedures_info %>%
  mutate(chartdate = as.POSIXct(chartdate, format="%Y-%m-%d %H:%M:%S")) %>%
  left_join(d_icd_procedures, by = c("icd_code", "icd_version"))

procedure_name_cols <- grep("long_title", colnames(procedures_info),
  value = TRUE)

if (length(procedure_name_cols) > 1) {
  procedures_info <- procedures_info %>% 
  select(-one_of(procedure_name_cols[-1])) %>% 
  rename(procedure_name = procedure_name_cols[1])
} else if (length(procedure_name_cols) == 1) {
  procedures_info <- procedures_info %>%
  rename(procedure_name = procedure_name_cols[1])
} else {
  stop("procedure_name column not found in procedures_info")
}

procedures_info <- procedures_info %>% filter(!is.na(procedure_name))

care_unit_colors <- c("Emergency Department" = "red", 
                      "Medicine" = "green", 
                      "Neurology" = "cyan", 
                      "Surgical Intensive Care Unit (SICU)" = "purple")

procedure_shapes <- setNames(seq(15, 15 +
  length(unique(procedures_info$procedure_name)) - 1),
  unique(procedures_info$procedure_name))

plot <- ggplot() +
  geom_segment(data = transfers_info, 
  aes(x = intime, xend = outtime,
  y = "ADT", yend = "ADT", color = careunit), 
  linewidth = 3) +
  geom_point(data = labevents_info, 
  aes(x = chartdate, y = "Lab"), shape = 3, size = 3) +
  geom_point(data = procedures_info, 
  aes(x = chartdate, y = "Procedure", shape = procedure_name), size = 5) +
  scale_color_manual(values = care_unit_colors) +
  scale_shape_manual(values = procedure_shapes, drop = FALSE) +
  theme_minimal() +
  labs(title = patient_summary, 
  subtitle = top_diagnoses_text, 
  x = "Calendar Time", 
  y = NULL, 
  color = "Care Unit", 
  shape = "Procedure")
print(plot)

Q1.2 ICU stays

Solution:

subject_id_of_interest <- 10063848
subject_stays <- icustays %>%
  filter(subject_id == subject_id_of_interest) %>%
  select(stay_id, intime, outtime)
chartevents_filtered <- chartevents_pq %>%
  filter(subject_id == subject_id_of_interest) %>%
  inner_join(subject_stays, by = "stay_id") %>%
  filter(charttime >= intime & charttime <= outtime) %>%
  select(stay_id, itemid, charttime, valuenum)
chartevents_with_labels <- chartevents_filtered %>%
  inner_join(d_items %>% select(itemid, abbreviation), by = "itemid")
chartevents_with_labels <- chartevents_with_labels %>%
  mutate(charttime = as_datetime(charttime))
ggplot(chartevents_with_labels, aes(x = charttime, y = valuenum,
                                    color = abbreviation)) +
  geom_point(size = 1.2) + 
  geom_line(size = 0.8) +  
  facet_grid(abbreviation ~ stay_id, scales = "free") +  
  labs(
    title = paste("Patient", subject_id_of_interest, "ICU stays - Vitals"),
    x = "Time",
    y = "Vital Value"
  ) +
  scale_x_datetime(
    breaks = seq(
      floor_date(min(chartevents_with_labels$charttime, na.rm = TRUE),
                 unit = "6 hours"),
      ceiling_date(max(chartevents_with_labels$charttime, na.rm = TRUE),
                   unit = "6 hours"),
      by = "6 hours"
    ),
    date_labels = "%b %d %H:%M" 
  ) +
  theme_minimal() +
  theme(
    legend.position = "none", 
    strip.text = element_text(size = 12, face = "bold", color = "white"), 
    strip.background = element_rect(fill = "darkgrey", color = "darkgrey"), 
    axis.text.x = element_text(angle = 0, hjust = 0.5),  
    panel.grid.major = element_line(size = 0.5, linetype = "dotted",
                                    color = "gray"),
    panel.grid.minor = element_blank()  
  )

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

ICU stays are a subset of ADT history. This figure shows the vitals of the patient 10001217 during ICU stays. The x-axis is the calendar time, and the y-axis is the value of the vital. The color of the line represents the type of vital. The facet grid shows the abbreviation of the vital and the stay ID. Do a similar visualization for the patient 10063848.

Q2. ICU stays

icustays.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/icustays/) contains data about Intensive Care Units (ICU) stays. The first 10 lines are

zcat < ~/mimic/icu/icustays.csv.gz | head

subject_id,hadm_id,stay_id,first_careunit,last_careunit,intime,outtime,los
10000032,29079034,39553978,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2180-07-23 14:00:00,2180-07-23 23:50:47,0.4102662037037037
10000690,25860671,37081114,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2150-11-02 19:37:00,2150-11-06 17:03:17,3.8932523148148146
10000980,26913865,39765666,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2189-06-27 08:42:00,2189-06-27 20:38:27,0.4975347222222222
10001217,24597018,37067082,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-11-20 19:18:02,2157-11-21 22:08:00,1.1180324074074075
10001217,27703517,34592300,Surgical Intensive Care Unit (SICU),Surgical Intensive Care Unit (SICU),2157-12-19 15:42:24,2157-12-20 14:27:41,0.948113425925926
10001725,25563031,31205490,Medical/Surgical Intensive Care Unit (MICU/SICU),Medical/Surgical Intensive Care Unit (MICU/SICU),2110-04-11 15:52:22,2110-04-12 23:59:56,1.338587962962963
10001843,26133978,39698942,Medical/Surgical Intensive Care Unit (MICU/SICU),Medical/Surgical Intensive Care Unit (MICU/SICU),2134-12-05 18:50:03,2134-12-06 14:38:26,0.8252662037037037
10001884,26184834,37510196,Medical Intensive Care Unit (MICU),Medical Intensive Care Unit (MICU),2131-01-11 04:20:05,2131-01-20 08:27:30,9.17181712962963
10002013,23581541,39060235,Cardiac Vascular Intensive Care Unit (CVICU),Cardiac Vascular Intensive Care Unit (CVICU),2160-05-18 10:00:53,2160-05-19 17:33:33,1.314351851851852

Q2.1 Ingestion

Import icustays.csv.gz as a tibble icustays_tble. Solution:

icustays_tble <- read_csv("~/mimic/icu/icustays.csv.gz")

Rows: 94458 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): first_careunit, last_careunit
dbl  (4): subject_id, hadm_id, stay_id, los
dttm (2): intime, outtime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Q2.2 Summary and visualization

How many unique subject_id? Can a subject_id have multiple ICU stays? Summarize the number of ICU stays per subject_id by graphs.

Solution: The number of unique values in subject_id

num_unique_subjects <- icustays_tble %>%
  distinct(subject_id) %>%
  count()

cat("Number of unique subjects:", num_unique_subjects$n, "\n")

Number of unique subjects: 65366 

Checking if a subject has had multiple ICU stays

icu_stay_counts <- icustays_tble %>%
  group_by(subject_id) %>%
  summarize(num_stays = n(), .groups = "drop")
cat("Max ICU stays by a single patient:", max(icu_stay_counts$num_stays), "\n")

Max ICU stays by a single patient: 41 

num_multiple_stays <- icu_stay_counts %>%
  filter(num_stays > 1) %>%
  count()
cat("Number of patients with multiple ICU stays:", num_multiple_stays$n, "\n")

Number of patients with multiple ICU stays: 16242 

Summarizeing the number of ICU stays per subject_id by graphs

# Bar Chart of ICU Stay Counts
icu_stay_counts %>%
  count(num_stays) %>%
  arrange(desc(n)) %>%
  head(20) %>%
  ggplot(aes(x = factor(num_stays), y = n)) +
  geom_bar(stat = "identity", fill = "steelblue", color = "black"
           , alpha = 0.8) +
  labs(
    title = "Frequency of ICU Stay Counts",
    x = "Number of ICU Stays",
    y = "Number of Patients"
  ) +
  theme_minimal()

# Cumulative Frequency Plot
ggplot(icu_stay_counts, aes(x = num_stays)) +
  stat_ecdf(geom = "step", color = "red", size = 1) +
  labs(
    title = "Cumulative Distribution of ICU Stays per Patient",
    x = "Number of ICU Stays",
    y = "Cumulative Proportion"
  ) +
  theme_minimal()

Q3. admissions data

Information of the patients admitted into hospital is available in admissions.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/admissions/ for details of each field in this file. The first 10 lines are

zcat < ~/mimic/hosp/admissions.csv.gz | head

subject_id,hadm_id,admittime,dischtime,deathtime,admission_type,admit_provider_id,admission_location,discharge_location,insurance,language,marital_status,race,edregtime,edouttime,hospital_expire_flag
10000032,22595853,2180-05-06 22:23:00,2180-05-07 17:15:00,,URGENT,P49AFC,TRANSFER FROM HOSPITAL,HOME,Medicaid,English,WIDOWED,WHITE,2180-05-06 19:17:00,2180-05-06 23:30:00,0
10000032,22841357,2180-06-26 18:27:00,2180-06-27 18:49:00,,EW EMER.,P784FA,EMERGENCY ROOM,HOME,Medicaid,English,WIDOWED,WHITE,2180-06-26 15:54:00,2180-06-26 21:31:00,0
10000032,25742920,2180-08-05 23:44:00,2180-08-07 17:50:00,,EW EMER.,P19UTS,EMERGENCY ROOM,HOSPICE,Medicaid,English,WIDOWED,WHITE,2180-08-05 20:58:00,2180-08-06 01:44:00,0
10000032,29079034,2180-07-23 12:35:00,2180-07-25 17:55:00,,EW EMER.,P06OTX,EMERGENCY ROOM,HOME,Medicaid,English,WIDOWED,WHITE,2180-07-23 05:54:00,2180-07-23 14:00:00,0
10000068,25022803,2160-03-03 23:16:00,2160-03-04 06:26:00,,EU OBSERVATION,P39NWO,EMERGENCY ROOM,,,English,SINGLE,WHITE,2160-03-03 21:55:00,2160-03-04 06:26:00,0
10000084,23052089,2160-11-21 01:56:00,2160-11-25 14:52:00,,EW EMER.,P42H7G,WALK-IN/SELF REFERRAL,HOME HEALTH CARE,Medicare,English,MARRIED,WHITE,2160-11-20 20:36:00,2160-11-21 03:20:00,0
10000084,29888819,2160-12-28 05:11:00,2160-12-28 16:07:00,,EU OBSERVATION,P35NE4,PHYSICIAN REFERRAL,,Medicare,English,MARRIED,WHITE,2160-12-27 18:32:00,2160-12-28 16:07:00,0
10000108,27250926,2163-09-27 23:17:00,2163-09-28 09:04:00,,EU OBSERVATION,P40JML,EMERGENCY ROOM,,,English,SINGLE,WHITE,2163-09-27 16:18:00,2163-09-28 09:04:00,0
10000117,22927623,2181-11-15 02:05:00,2181-11-15 14:52:00,,EU OBSERVATION,P47EY8,EMERGENCY ROOM,,Medicaid,English,DIVORCED,WHITE,2181-11-14 21:51:00,2181-11-15 09:57:00,0

Q3.1 Ingestion

Import admissions.csv.gz as a tibble admissions_tble.

Solution:

admissions_tble <- read_csv("~/mimic/hosp/admissions.csv.gz")

Rows: 546028 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (8): admission_type, admit_provider_id, admission_location, discharge_l...
dbl  (3): subject_id, hadm_id, hospital_expire_flag
dttm (5): admittime, dischtime, deathtime, edregtime, edouttime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Q3.2 Summary and visualization

Summarize the following information by graphics and explain any patterns you see.

• number of admissions per patient
  
• admission hour (anything unusual?)
  
• admission minute (anything unusual?)
  
• length of hospital stay (from admission to discharge) (anything unusual?)

According to the MIMIC-IV documentation,

All dates in the database have been shifted to protect patient confidentiality. Dates will be internally consistent for the same patient, but randomly distributed in the future. Dates of birth which occur in the present time are not true dates of birth. Furthermore, dates of birth which occur before the year 1900 occur if the patient is older than 89. In these cases, the patient’s age at their first admission has been fixed to 300.

Solution: Number of admissions per patient

admissions_tble <- admissions_tble %>%
  mutate(
    admission_hour = hour(admittime),
    admission_minute = minute(admittime),
    los_days = as.numeric(difftime(dischtime, admittime, units = "days"))
  )

admissions_tble <- admissions_tble %>%
  mutate(
    admission_hour = hour(admittime),
    admission_minute = minute(admittime),
    los_days = as.numeric(difftime(dischtime, admittime, units = "days"))
  )


summary(admissions_tble$admission_hour)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    7.00   15.00   13.01   19.00   23.00 

summary(admissions_tble$admission_minute)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   13.00   28.00   27.85   43.00   59.00 

summary(admissions_tble$los_days)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 -0.9451   1.1285   2.8181   4.7617   5.6215 515.5625 

Explanation of Patterns:
Admission Hour: There is a peak around 0 AM and 7 AM, which is unusual
0 AM might reflect end-of-day admissions or data recording, while 7 AM may be related to shift changes.
Admission Minute: The 15-minute peak intervals (e.g., 15, 30, 45) might be due to rounding in documentation.
Length of Stay: Most patients stay between 1 and 5 days, with outliers staying longer due to severe conditions.

Admission hour

ggplot(admissions_tble, aes(x = admission_hour)) +
  geom_histogram(binwidth = 1, fill = "darkorange", color = "black") +
  theme_minimal() +
  labs(title = "Distribution of Admission Hours", x = "Hour of Admission", y = "Frequency") 

Admission minute

ggplot(admissions_tble, aes(x = admission_minute)) +
  geom_histogram(binwidth = 1, fill = "purple", color = "black") +
  theme_minimal() +
  labs(title = "Distribution of Admission Minutes", x = "Minute of Admission", y = "Frequency") +
  annotate("text", x = 15, y = 20000, label = "15-min intervals", color = "blue")

Length of hospital stay (from admission to discharge)

ggplot(admissions_tble, aes(x = los_days)) +
  geom_histogram(binwidth = 1, fill = "seagreen", color = "black") +
  theme_minimal() +
  labs(title = "Distribution of Length of Hospital Stay", x = "Length of Stay (days)", y = "Frequency") +
  annotate("text", x = 5, y = 100000, label = "Most stay within 5 days", color = "darkgreen")

Q4. patients data

Patient information is available in patients.csv.gz. See https://mimic.mit.edu/docs/iv/modules/hosp/patients/ for details of each field in this file. The first 10 lines are

zcat < ~/mimic/hosp/patients.csv.gz | head

subject_id,gender,anchor_age,anchor_year,anchor_year_group,dod
10000032,F,52,2180,2014 - 2016,2180-09-09
10000048,F,23,2126,2008 - 2010,
10000058,F,33,2168,2020 - 2022,
10000068,F,19,2160,2008 - 2010,
10000084,M,72,2160,2017 - 2019,2161-02-13
10000102,F,27,2136,2008 - 2010,
10000108,M,25,2163,2014 - 2016,
10000115,M,24,2154,2017 - 2019,
10000117,F,48,2174,2008 - 2010,

Q4.1 Ingestion

Import patients.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/patients/) as a tibble patients_tble.

Solution:

patients_tble <- read_csv("~/mimic/hosp/patients.csv.gz")

Rows: 364627 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): gender, anchor_year_group
dbl  (3): subject_id, anchor_age, anchor_year
date (1): dod

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(patients_tble)

Rows: 364,627
Columns: 6
$ subject_id        <dbl> 10000032, 10000048, 10000058, 10000068, 10000084, 10…
$ gender            <chr> "F", "F", "F", "F", "M", "F", "M", "M", "F", "M", "F…
$ anchor_age        <dbl> 52, 23, 33, 19, 72, 27, 25, 24, 48, 60, 59, 34, 20, …
$ anchor_year       <dbl> 2180, 2126, 2168, 2160, 2160, 2136, 2163, 2154, 2174…
$ anchor_year_group <chr> "2014 - 2016", "2008 - 2010", "2020 - 2022", "2008 -…
$ dod               <date> 2180-09-09, NA, NA, NA, 2161-02-13, NA, NA, NA, NA,…

Q4.2 Summary and visualization

Summarize variables gender and anchor_age by graphics, and explain any patterns you see.

Solution:

# Gender Distribution
ggplot(patients_tble, aes(x = gender)) +
  geom_bar(fill = "#F8766D") +
  labs(title = "Gender Distribution of Patients", x = "Gender", y = "Count") +
  theme_minimal()

# Anchor Age Distribution
ggplot(patients_tble, aes(x = anchor_age)) +
  geom_histogram(binwidth = 5, fill = "#619CFF", color = "black", alpha = 0.7) +
  labs(title = "Distribution of Patient Ages", x = "Anchor Age", y = "Count") +
  theme_minimal()

Explanation of Gender Distribution:
The dataset has a fairly balanced gender distribution, with slightly more females than males.
Explanation of Anchor Age Distribution:
The age distribution shows that most patients are between 50 and 80 years old, which is expected given the ICU setting.
The sharp peak at age 91 is due to the MIMIC-IV data de-identification process, where patients older than 89 are all assigned an age of 91.

Q5. Lab results

labevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/labevents/) contains all laboratory measurements for patients. The first 10 lines are

zcat < ~/mimic/hosp/labevents.csv.gz | head

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,2704548,50931,P69FQC,2180-03-23 11:51:00,2180-03-23 15:56:00,___,95,mg/dL,70,100,,ROUTINE,"IF FASTING, 70-100 NORMAL, >125 PROVISIONAL DIABETES."
2,10000032,,36092842,51071,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
3,10000032,,36092842,51074,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
4,10000032,,36092842,51075,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"BENZODIAZEPINE IMMUNOASSAY SCREEN DOES NOT DETECT SOME DRUGS,;INCLUDING LORAZEPAM, CLONAZEPAM, AND FLUNITRAZEPAM."
5,10000032,,36092842,51079,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
6,10000032,,36092842,51087,P69FQC,2180-03-23 11:51:00,,,,,,,,ROUTINE,RANDOM.
7,10000032,,36092842,51089,P69FQC,2180-03-23 11:51:00,2180-03-23 16:15:00,,,,,,,ROUTINE,PRESUMPTIVELY POSITIVE.
8,10000032,,36092842,51090,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,METHADONE ASSAY DETECTS ONLY METHADONE (NOT OTHER OPIATES/OPIOIDS).
9,10000032,,36092842,51092,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"OPIATE IMMUNOASSAY SCREEN DOES NOT DETECT SYNTHETIC OPIOIDS;SUCH AS METHADONE, OXYCODONE, FENTANYL, BUPRENORPHINE, TRAMADOL,;NALOXONE, MEPERIDINE.  SEE ONLINE LAB MANUAL FOR DETAILS."

d_labitems.csv.gz (https://mimic.mit.edu/docs/iv/modules/hosp/d_labitems/) is the dictionary of lab measurements.

zcat < ~/mimic/hosp/d_labitems.csv.gz | head

itemid,label,fluid,category
50801,Alveolar-arterial Gradient,Blood,Blood Gas
50802,Base Excess,Blood,Blood Gas
50803,"Calculated Bicarbonate, Whole Blood",Blood,Blood Gas
50804,Calculated Total CO2,Blood,Blood Gas
50805,Carboxyhemoglobin,Blood,Blood Gas
50806,"Chloride, Whole Blood",Blood,Blood Gas
50808,Free Calcium,Blood,Blood Gas
50809,Glucose,Blood,Blood Gas
50810,"Hematocrit, Calculated",Blood,Blood Gas

We are interested in the lab measurements of creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931). Retrieve a subset of labevents.csv.gz that only containing these items for the patients in icustays_tble. Further restrict to the last available measurement (by storetime) before the ICU stay. The final labevents_tble should have one row per ICU stay and columns for each lab measurement.

Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make labevents_pq folder available at the current working directory hw3, for example, by a symbolic link.

Solution:

d_labitems_tble <- read_csv("~/mimic/hosp/d_labitems.csv.gz") %>%
  filter(itemid %in% c(50912, 50971, 50983, 50902, 50882, 51221, 51301, 50931)) 

Rows: 1650 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): label, fluid, category
dbl (1): itemid

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

labevents_tble <- open_dataset(sources = "labevents_pq/part-0.parquet", format = "parquet") |> 
  to_duckdb() |> 
  select(subject_id, itemid, storetime, valuenum) |> 
  filter(itemid %in% local(d_labitems_tble$itemid)) |> 
  left_join(select(icustays_tble, subject_id, stay_id, intime), by = c("subject_id" = "subject_id"), copy = TRUE) |> 
  filter(storetime < intime) |> 
  group_by(subject_id, stay_id, itemid) |> 
  slice_max(storetime, n = 1) |> 
  select(-storetime, -intime) |> 
  ungroup() |> 
  pivot_wider(names_from = itemid, values_from = valuenum) |> 
  rename_with(~ str_to_lower(d_labitems_tble$label[match(.x, as.character(d_labitems_tble$itemid))]), .cols = everything()) |> 
  collect() |> 
  arrange(subject_id, stay_id) |> 
  relocate(subject_id, stay_id, .before = everything())
cat("Final number of rows:", nrow(labevents_tble), "\n")

Final number of rows: 88086 

Q6. Vitals from charted events

chartevents.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/chartevents/) contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226512,39.4,39.4,kg,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226707,60,60,Inch,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226730,152,152,cm,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,220048,SR (Sinus Rhythm),,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224642,Oral,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224650,None,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:20:00,223761,98.7,98.7,°F,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220179,84,84,mmHg,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220180,48,48,mmHg,0

d_items.csv.gz (https://mimic.mit.edu/docs/iv/modules/icu/d_items/) is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

We are interested in the vitals for ICU patients: heart rate (220045), systolic non-invasive blood pressure (220179), diastolic non-invasive blood pressure (220180), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items for the patients in icustays_tble. Further restrict to the first vital measurement within the ICU stay. The final chartevents_tble should have one row per ICU stay and columns for each vital measurement.

Hint: Use the Parquet format you generated in Homework 2. For reproducibility, make chartevents_pq folder available at the current working directory, for example, by a symbolic link.

Solution:

vital_ids <- c(
  "heart_rate" = 220045,
  "non_invasive_blood_pressure_systolic" = 220179,
  "non_invasive_blood_pressure_diastolic" = 220180,
  "temperature_fahrenheit" = 223761,
  "respiratory_rate" = 220210
)

subset_chartevents <- chartevents_pq %>%
  mutate(
    storetime = as.POSIXct(storetime, format = "%Y-%m-%d %H:%M:%S", tz = "UTC"),
    charttime = as.POSIXct(charttime, format = "%Y-%m-%d %H:%M:%S", tz = "UTC")
  )

chartevents_filtered <- subset_chartevents %>%
  inner_join(icustays_tble, by = "stay_id") %>%
  filter(storetime >= intime & storetime < outtime)

chartevents_filtered <- chartevents_filtered %>%
  rename(subject_id = subject_id.x) %>%
  select(-subject_id.y)

first_store_per_stay <- chartevents_filtered %>%
  group_by(subject_id, stay_id, itemid) %>%
  arrange(storetime) %>%
  slice_min(order_by = storetime, n = 1) %>%
  ungroup()

chartevents_filtered <- chartevents_filtered %>%
  inner_join(
    first_store_per_stay %>% select(subject_id, stay_id, itemid, storetime),
    by = c("subject_id", "stay_id", "itemid", "storetime")
  ) %>%
  select(-storetime)

Warning in inner_join(., first_store_per_stay %>% select(subject_id, stay_id, : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 44 of `x` matches multiple rows in `y`.
ℹ Row 6 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

chartevents_filtered <- chartevents_filtered %>%
  group_by(subject_id, stay_id, itemid) %>%
  summarize(valuenum_avg = mean(valuenum, na.rm = TRUE), .groups = "drop") %>%
  ungroup()

chartevents_tble <- chartevents_filtered %>%
  pivot_wider(
    names_from = itemid,
    values_from = valuenum_avg,
    names_prefix = "vital_"
  ) %>%
  rename(
    heart_rate = vital_220045,
    non_invasive_blood_pressure_systolic = vital_220179,
    non_invasive_blood_pressure_diastolic = vital_220180,
    temperature_fahrenheit = vital_223761,
    respiratory_rate = vital_220210
  ) %>%
  arrange(subject_id, stay_id)

cat("Final number of rows:", nrow(chartevents_tble), "\n")

Final number of rows: 94363 

Q7. Putting things together

Let us create a tibble mimic_icu_cohort for all ICU stays, where rows are all ICU stays of adults (age at intime >= 18) and columns contain at least following variables

• all variables in icustays_tble
  
• all variables in admissions_tble
  
• all variables in patients_tble
• the last lab measurements before the ICU stay in labevents_tble
• the first vital measurements during the ICU stay in chartevents_tble

The final mimic_icu_cohort should have one row per ICU stay and columns for each variable.

Solution:

icustays_age <- icustays_tble %>%
  mutate(intime_year = lubridate::year(as.Date(intime)))

age_at_intime <- icustays_age %>%
  left_join(patients_tble %>% select(subject_id, anchor_age, anchor_year), by = "subject_id") %>%
  mutate(age_at_intime = anchor_age + (intime_year - anchor_year)) %>%
  select(subject_id, stay_id, age_at_intime)

icustays_filtered <- icustays_tble %>%
  left_join(age_at_intime, by = c("subject_id", "stay_id")) %>%
  inner_join(patients_tble %>%
               select(subject_id, anchor_age, anchor_year, anchor_year_group, dod, gender),
             by = "subject_id") %>%
  filter(age_at_intime >= 18)

admissions_selected <- admissions_tble

vitals_selected <- chartevents_tble
labs_selected <- labevents_tble

mimic_icu_cohort <- icustays_filtered %>%
  left_join(admissions_selected, by = c("subject_id", "hadm_id")) %>%
  left_join(vitals_selected, by = c("subject_id", "stay_id")) %>%
  left_join(labs_selected, by = c("subject_id", "stay_id")) %>%
  distinct() %>%
  arrange(subject_id, hadm_id, stay_id)

print(mimic_icu_cohort)

# A tibble: 94,458 × 44
   subject_id  hadm_id  stay_id first_careunit last_careunit intime             
        <dbl>    <dbl>    <dbl> <chr>          <chr>         <dttm>             
 1   10000032 29079034 39553978 Medical Inten… Medical Inte… 2180-07-23 14:00:00
 2   10000690 25860671 37081114 Medical Inten… Medical Inte… 2150-11-02 19:37:00
 3   10000980 26913865 39765666 Medical Inten… Medical Inte… 2189-06-27 08:42:00
 4   10001217 24597018 37067082 Surgical Inte… Surgical Int… 2157-11-20 19:18:02
 5   10001217 27703517 34592300 Surgical Inte… Surgical Int… 2157-12-19 15:42:24
 6   10001725 25563031 31205490 Medical/Surgi… Medical/Surg… 2110-04-11 15:52:22
 7   10001843 26133978 39698942 Medical/Surgi… Medical/Surg… 2134-12-05 18:50:03
 8   10001884 26184834 37510196 Medical Inten… Medical Inte… 2131-01-11 04:20:05
 9   10002013 23581541 39060235 Cardiac Vascu… Cardiac Vasc… 2160-05-18 10:00:53
10   10002114 27793700 34672098 Coronary Care… Coronary Car… 2162-02-17 23:30:00
# ℹ 94,448 more rows
# ℹ 38 more variables: outtime <dttm>, los <dbl>, age_at_intime <dbl>,
#   anchor_age <dbl>, anchor_year <dbl>, anchor_year_group <chr>, dod <date>,
#   gender <chr>, admittime <dttm>, dischtime <dttm>, deathtime <dttm>,
#   admission_type <chr>, admit_provider_id <chr>, admission_location <chr>,
#   discharge_location <chr>, insurance <chr>, language <chr>,
#   marital_status <chr>, race <chr>, edregtime <dttm>, edouttime <dttm>, …

colnames(mimic_icu_cohort)

 [1] "subject_id"                           
 [2] "hadm_id"                              
 [3] "stay_id"                              
 [4] "first_careunit"                       
 [5] "last_careunit"                        
 [6] "intime"                               
 [7] "outtime"                              
 [8] "los"                                  
 [9] "age_at_intime"                        
[10] "anchor_age"                           
[11] "anchor_year"                          
[12] "anchor_year_group"                    
[13] "dod"                                  
[14] "gender"                               
[15] "admittime"                            
[16] "dischtime"                            
[17] "deathtime"                            
[18] "admission_type"                       
[19] "admit_provider_id"                    
[20] "admission_location"                   
[21] "discharge_location"                   
[22] "insurance"                            
[23] "language"                             
[24] "marital_status"                       
[25] "race"                                 
[26] "edregtime"                            
[27] "edouttime"                            
[28] "hospital_expire_flag"                 
[29] "admission_hour"                       
[30] "admission_minute"                     
[31] "los_days"                             
[32] "heart_rate"                           
[33] "non_invasive_blood_pressure_systolic" 
[34] "non_invasive_blood_pressure_diastolic"
[35] "respiratory_rate"                     
[36] "temperature_fahrenheit"               
[37] "glucose"                              
[38] "hematocrit"                           
[39] "creatinine"                           
[40] "sodium"                               
[41] "bicarbonate"                          
[42] "white blood cells"                    
[43] "potassium"                            
[44] "chloride"                             

Q8. Exploratory data analysis (EDA)

Summarize the following information about the ICU stay cohort mimic_icu_cohort using appropriate numerics or graphs:

Solution: - Length of ICU stay los vs demographic variables (race, insurance, marital_status, gender, age at intime)

mimic_icu_cohort %>%
  select(los, race, insurance, marital_status, gender, age_at_intime) %>%
  summary()

      los                race            insurance         marital_status    
 Min.   :  0.00125   Length:94458       Length:94458       Length:94458      
 1st Qu.:  1.09621   Class :character   Class :character   Class :character  
 Median :  1.96565   Mode  :character   Mode  :character   Mode  :character  
 Mean   :  3.63002                                                           
 3rd Qu.:  3.86258                                                           
 Max.   :226.40308                                                           
 NA's   :14                                                                  
    gender          age_at_intime   
 Length:94458       Min.   : 18.00  
 Class :character   1st Qu.: 55.00  
 Mode  :character   Median : 66.00  
                    Mean   : 64.79  
                    3rd Qu.: 77.00  
                    Max.   :103.00  
                                    

ggplot(mimic_icu_cohort, aes(x = marital_status, y = los)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", color = "blue") +
  labs(title = "ICU Length of Stay vs Marital_Status", x = "Marital_Status",
       y = "Length of Stay (days)")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 14 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(mimic_icu_cohort, aes(x = age_at_intime, y = los)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", color = "blue") +
  labs(title = "ICU Length of Stay vs Age", x = "Age",
       y = "Length of Stay (days)")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_smooth()`).
Removed 14 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(mimic_icu_cohort, aes(x = race, y = los, fill = race)) +
  geom_boxplot() +
  labs(title = "ICU Length of Stay vs Race", x = "Race",
       y = "Length of Stay (days)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_boxplot()`).

ggplot(mimic_icu_cohort, aes(x = gender, y = los, fill = gender)) +
  geom_boxplot() +
  labs(title = "ICU Length of Stay vs Gender", x = "Gender",
       y = "Length of Stay (days)")

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_boxplot()`).

ggplot(mimic_icu_cohort, aes(x = insurance, y = los, fill = insurance)) +
  geom_boxplot() +
  labs(title = "ICU Length of Stay vs Insurance", x = "Insurance",
       y = "Length of Stay (days)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_boxplot()`).

• Length of ICU stay los vs the last available lab measurements before ICU stay

mimic_icu_cohort %>%
  select(los, `white blood cells`, hematocrit, creatinine, sodium, glucose,
         potassium, chloride, bicarbonate) %>%
  pivot_longer(cols = -los, names_to = "Lab", values_to = "Value") %>%
  ggplot(aes(x = Value, y = los, color = Lab)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  facet_wrap(~ Lab, scales = "free_x") +
  labs(title = "ICU Length of Stay vs Last Lab Measurements",
       x = "Lab Value", y = "Length of Stay (days)")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 79011 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 79011 rows containing missing values or values outside the scale range
(`geom_point()`).

• Length of ICU stay los vs the first vital measurements within the ICU stay

mimic_icu_cohort %>%
  select(los, heart_rate, non_invasive_blood_pressure_systolic,
         non_invasive_blood_pressure_diastolic, 
         temperature_fahrenheit, respiratory_rate) %>%
  cor(use = "complete.obs")

                                                los  heart_rate
los                                    1.0000000000 0.067042611
heart_rate                             0.0670426114 1.000000000
non_invasive_blood_pressure_systolic  -0.0037026394 0.001906456
non_invasive_blood_pressure_diastolic  0.0005899571 0.015957059
temperature_fahrenheit                 0.0062384099 0.061290942
respiratory_rate                       0.0886158884 0.353521841
                                      non_invasive_blood_pressure_systolic
los                                                           -0.003702639
heart_rate                                                     0.001906456
non_invasive_blood_pressure_systolic                           1.000000000
non_invasive_blood_pressure_diastolic                          0.002012290
temperature_fahrenheit                                         0.001348258
respiratory_rate                                               0.006628126
                                      non_invasive_blood_pressure_diastolic
los                                                            0.0005899571
heart_rate                                                     0.0159570593
non_invasive_blood_pressure_systolic                           0.0020122897
non_invasive_blood_pressure_diastolic                          1.0000000000
temperature_fahrenheit                                         0.0022057585
respiratory_rate                                               0.0119027496
                                      temperature_fahrenheit respiratory_rate
los                                              0.006238410      0.088615888
heart_rate                                       0.061290942      0.353521841
non_invasive_blood_pressure_systolic             0.001348258      0.006628126
non_invasive_blood_pressure_diastolic            0.002205759      0.011902750
temperature_fahrenheit                           1.000000000      0.037346642
respiratory_rate                                 0.037346642      1.000000000

mimic_icu_cohort %>%
  select(los, heart_rate, non_invasive_blood_pressure_systolic,
         non_invasive_blood_pressure_diastolic, 
         temperature_fahrenheit, respiratory_rate) %>%
  pivot_longer(-los, names_to = "Vital", values_to = "Value") %>%
  ggplot(aes(x = Value, y = los, color = Vital)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm") +
  facet_wrap(~Vital, scales = "free_x") +
  labs(title = "ICU Length of Stay vs First Vital Measurements",
       x = "Vital Value", y = "Length of Stay (days)")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 4774 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 4774 rows containing missing values or values outside the scale range
(`geom_point()`).

• Length of ICU stay los vs first ICU unit

mimic_icu_cohort %>%
  group_by(first_careunit) %>%
  summarise(mean_los = mean(los, na.rm = TRUE), 
            median_los = median(los, na.rm = TRUE), 
            sd_los = sd(los, na.rm = TRUE), 
            count = n())

# A tibble: 17 × 5
   first_careunit                               mean_los median_los sd_los count
   <chr>                                           <dbl>      <dbl>  <dbl> <int>
 1 Cardiac Vascular Intensive Care Unit (CVICU)     3.32       1.99   4.99 14771
 2 Coronary Care Unit (CCU)                         3.09       2.01   3.57 10775
 3 Intensive Care Unit (ICU)                        8.79       5.76  10.3     33
 4 Med/Surg                                         1.44       1.44  NA        1
 5 Medical Intensive Care Unit (MICU)               3.76       1.91   5.91 20703
 6 Medical/Surgical Intensive Care Unit (MICU/…     3.09       1.79   4.59 15449
 7 Medicine                                        15.8       13.8   11.4     16
 8 Medicine/Cardiology Intermediate                 2.58       2.58  NA        1
 9 Neuro Intermediate                               5.02       3.00   6.05  5776
10 Neuro Stepdown                                   4.07       2.20   5.30  1421
11 Neuro Surgical Intensive Care Unit (Neuro S…     4.48       2.24   6.46  1751
12 Neurology                                       28.2       28.2   NA        1
13 PACU                                             4.02       2.00   5.65   122
14 Surgery/Trauma                                  10.6       11.6    6.63    10
15 Surgery/Vascular/Intermediate                   15.7       13.7   12.2    145
16 Surgical Intensive Care Unit (SICU)              3.90       1.98   6.15 13009
17 Trauma SICU (TSICU)                              3.64       1.88   5.42 10474

ggplot(mimic_icu_cohort, aes(x = first_careunit,
                             y = los, fill = first_careunit)) +
  geom_boxplot() +
  labs(title = "ICU Length of Stay vs First ICU Unit", x = "First ICU Unit",
       y = "Length of Stay (days)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Warning: Removed 14 rows containing non-finite outside the scale range
(`stat_boxplot()`).